Author: Zaixing Shi; Ya Fang
Title: Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China Document date: 2020_3_17
ID: hrrzztt5_26
Snippet: After prewhitening the COVID-19 incidence time series by the ARIMA model fitted on the outbound traffic time series, we calculated the cross-correlation coefficients between daily outbound traffic volume and COVID-19 incidence for each province. There were great geographical variations in time lags and correlation coefficients (Figure 2A-2B) . K-means clustering analysis identified 3 latent clusters of provinces according to time lag and correlat.....
Document: After prewhitening the COVID-19 incidence time series by the ARIMA model fitted on the outbound traffic time series, we calculated the cross-correlation coefficients between daily outbound traffic volume and COVID-19 incidence for each province. There were great geographical variations in time lags and correlation coefficients (Figure 2A-2B) . K-means clustering analysis identified 3 latent clusters of provinces according to time lag and correlation coefficients ( Figure 2C ). The estimated time lags between traffic volume and COVID-19 incidence were <1 week in 42% of provinces, 1 week in 39% of provinces, and 2-3 weeks in 19% of provinces.
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